Be an internal champion for Deep Learning and HPC among the Nvidia technical community. You will assist field business development in guiding the customer...From NVIDIA - Wed, 18 Jul 2018 07:54:45 GMT - View all Santa Clara, CA jobs

Smart mobile devices and mobile apps have been rolling out at swift speeds over the last decade, turning these devices into convenient and general-purpose computing platforms. Sensory data from smart devices are important resources to nourish mobile services, and they are regarded as innocuous information that can be obtained without user permissions. In this article, we show that this seemingly innocuous information could cause serious privacy issues. First, we demonstrate that users' tap positions on the screens of smart devices can be identified based on sensory data by employing some deep learning techniques. Second, it is shown that tap stream profiles for each type of apps can be collected, so that a user's app usage habit can be accurately inferred. In our experiments, the sensory data and mobile app usage information of 102 volunteers are collected. The experiment results demonstrate that the prediction accuracy of tap position inference can be at least 90 percent by utilizing convolutional neural networks. Furthermore, based on the inferred tap position information, users' app usage habits and passwords may be inferred with high accuracy.

While mobile social networks (MSNs) enrich people's lives, they also bring many security issues. Many attackers spread malicious URLs through MSNs, which causes serious threats to users' privacy and security. In order to provide users with a secure social environment, many researchers make great efforts. The majority of existing work is aimed at deploying a detection system on the server and classifying messages or users in MSNs through graph-based algorithms, machine learning or other methods. However, as a kind of instant messaging service, MSNs continually generate a large amount of user data. Without affecting the user experience, with existing detection mechanisms it is difficult to implement real-time detection in practical applications. In order to realize real-time message detection in MSNs, we can build more powerful server clusters or improve the utilization rate of computing resources. Assuming that computing resources of servers are limited, we use edge computing to improve the utilization rate of computing resources. In this article, we propose a multistage and elastic detection framework based on deep learning, which sets up a detection system at the mobile terminal and the server, respectively. Messages are first detected on the mobile terminal, and then the detection results are forwarded to the server along with the messages. We also design a detection queue, according to which the server can detect messages elastically when computing resources are limited, and more computing resources can be used for detecting more suspicious messages. We evaluate our detection framework on a Sina Weibo dataset. The results of the experiment show that our detection framework can improve the utilization rate of computing resources and can realize real-time detection with a high detection rate at a low false positive rate.

Deep computation model, as a tensor deep learning model, outperforms multi-modal deep learning models for feature learning on heterogenous data. However, deep computation model is limited in generalization to small heterogeneous data sets since it typically requires many training objects to learn the parameters. In this article, we propose a dropconnect deep computation model (DDCM) for highly heterogeneous data feature learning in mobile sensing networks. Specifically, the dropconnect technique is used to generalize the large fully-connected layers in the deep computation model for small heterogeneous data sets. Furthermore, the rectified linear units (ReLU) are used as the activation function to reduce computation and prevent overfitting. Finally, we compare the classification accuracy and execution time for learning the parameters between our model and the traditional deep computation model on two highly heterogeneous data sets. Results illustrate that our model achieves 2 percent higher classification accuracy and performs more efficiently than the deep computation model, proving the potential of our proposed model for highly heterogeneous data learning in mobile sensing networks.

Information about vehicle safety, such as the driving safety status and the road safety index, is of great importance to protect humans and support safe driving route planning. Despite some research on driving safety analysis, the accuracy and granularity of driving safety assessment are both very limited. Also, the problem of precisely and dynamically predicting road safety throughout a city has not been sufficiently studied and remains open. With the proliferation of sensor-equipped vehicles and smart devices, a huge amount of mobile sensing data provides an opportunity to conduct vehicle safety analysis. In this article, we first discuss mobile sensing data collection in VANETs and then identify two main challengs in vehicle safety analysis in VANETs, i.e., driving safety analysis and road safety analysis. In each issue, we review and classify the state-of-theart vehicle safety analysis techniques into different categories. For each category, a short description is given followed by a discussion of limitations. In order to improve vehicle safety, we propose a new deep learning framework (DeepRSI) to conduct real-time road safety prediction from the data mining perspective. Specifically, the proposed framework considers the spatio-temporal relationship of vehicle GPS trajectories and external environment factors. The evaluation results demonstrate the advantages of our proposed scheme over other methods by utilizing mobile sensing data collected in VANETs.

Traffic information is of great importance for urban cities, and accurate prediction of urban traffics has been pursued for many years. Urban traffic prediction aims to exploit sophisticated models to capture hidden traffic characteristics from substantial historical mobility data and then makes use of trained models to predict traffic conditions in the future. Due to the powerful capabilities of representation learning and feature extraction, emerging deep learning becomes a potent alternative for such traffic modeling. In this article, we envision the potential and broard usage of deep learning in predictions of various traffic indicators, for example, traffic speed, traffic flow, and accident risk. In addition, we summarize and analyze some early attempts that have achieved notable performance. By discussing these existing advances, we propose two future research directions to improve the accuracy and efficiency of urban traffic prediction on a large scale.

The emergence of MCS technologies provides a cost-efficient solution to accommodate large-scale sensing tasks. However, despite the potential benefits of MCS, there are several critical issues that remain to be solved, such as lack of incentive-compatible mechanisms for recruiting participants, lack of data validation, and high traffic load and latency. This motivates us to develop robust mobile crowd sensing (RMCS), a framework that integrates deep learning based data validation and edge computing based local processing. First, we present a comprehensive state-of-the-art literature review. Then, the conceptual design architecture of RMCS and practical implementations are described in detail. Next, a case study of smart transportation is provided to demonstrate the feasibility of the proposed RMCS framework. Finally, we identify several open issues and conclude the article.

TensorFlow.js
is a new version of the popular open-source library which brings deep learning to javascript. Developers can now define, train, and run machine learning models using the high-level library API
.

Pre-trained models
mean developers can now easily perform complex tasks like visual recognition
, generating music
or detecting human poses
with just a few lines of JavaScript.

Having started as a front-end library for web browsers, recent updates added experimental support
for Node.js. This allows TensorFlow.js to be used in backend JavaScript applications without having to use python.

Reading about the library, I wanted to test it out with a simple task…

Use TensorFlow.js to perform visual recognition on images using JavaScript from Node.js

Unfortunately, most of the documentation
and example code
provided uses the library in a browser. Project utilities
provided to simplify loading and using pre-trained models have not yet been extended with Node.js support. Getting this working did end up with me spending a lot of time reading the Typescript source files for the library. :-1:

However, after a few days’ hacking, I managed to get this completed
! Hurrah!

Before we dive into the code, let’s start with an overview of the different TensorFlow libraries.

Another excellent piece from Jason, suggest you join up with his service:

Jason Brownlee writes: What neural network is appropriate for your predictive modeling problem?It can be difficult for a beginner to the field of deep learning to know what type of network to use. There are so many types of networks to choose from and new methods being published and discussed every day.To make things worse, most neural networks are flexible enough that they work (make a prediction) even when used with the wrong type of data or prediction problem.In this post, you will discover the suggested use for the three main classes of artificial neural networks.After reading this post, you will know:Which types of neural networks to focus on when working on a predictive modeling problem.When to use, not use, and possible try using an MLP, CNN, and RNN on a project. ... To consider the use of hybrid models and to have a clear idea of your project goals before selecting a model.Let’s get started. ... "

The future of antivirus protection is exciting. Much like our cars, trains, and boats, the future of antivirus runs on artificial intelligence. AI technology is one of the fastest growing sectors around the world and security researchers are continually evaluating and integrating the technology into their consumer products.

Consumer antivirus products with AI or machine learning elements are appearing thick and fast. Does your next antivirus subscription need to include AI, or is it just another security buzzword? Let’s take a look.

Traditional Antivirus vs. AI Antivirus

The term “artificial intelligence” once conjured fantastical images of futuristic technology, but AI is now a reality. To understand what AI antivirus is, you need to understand how traditional antivirus works.

Traditional Antivirus

A traditional antivirus uses file and data signatures, and pattern analysis to compare potential malicious activity to previous instances. That is, the antivirus knows what the malicious file looks like, and can move swiftly to stop those files from infecting your system, should you pick one up. That’s a very basic explanation. You can read more about how it works and what scans to use right here The 3 Types of Antivirus Scans and When to Use Each One The 3 Types of Antivirus Scans and When to Use Each One Scanning your system with an antivirus program is important for keeping your system secure. But which type of antivirus scan should you use? Full, Quick, or Custom? Read More .

The antivirus on your system works well, don’t get me wrong. However, the number of malware attacks continues to rise, and security researchers regularly discover extremely advanced malware variants, such as Mylobot What Is Mylobot Malware? How It Works and What to Do About It What Is Mylobot Malware? How It Works and What to Do About It Every so often, a truly new malware strain appears. Mylobot is a perfect example. Learn more about what it is, why it's dangerous, and what to do about it. Read More . Furthermore, some traditional or legacy antivirus solutions cannot compete with advanced threats such as the devastating WannaCry ransomworm The Global Ransomware Attack and How to Protect Your Data The Global Ransomware Attack and How to Protect Your Data A massive cyberattack has struck computers around the globe. Have you been affected by the highly virulent self-replicating ransomware? If not, how can you protect your data without paying the ransom? Read More , or the Petya ransomware that encrypts your Master Boot Record Will The Petya Ransomware Crack Bring Back Your Files? Will The Petya Ransomware Crack Bring Back Your Files? A new ransomware variant, Petya, has been cracked by an irate victim. This is a chance to get one over on the cybercriminals, as we show you how to unlock your ransomed data. Read More .

As the threat landscape shifts, so must the antivirus detection mechanisms.

AI Antivirus

AI antivirus (or in some cases, machine learning―more on this distinction in a moment) works differently. There are a few different approaches, but AI antivirus learns about specific threats within its network environment and executes defensive activities without prompt.

AI and machine learning antivirus leverage sophisticated mathematical algorithms combined with the data from other deployments to understand what the baseline of security is for a given system. As well as this, they learn how to react to files that step outside that window of normal functionality.

Machine Learning vs. Artificial Intelligence

Another important distinction in the future of antivirus is between machine learning algorithms and artificial intelligence. The two words are sometimes used interchangeably but are not the same thing.

Artificial Intelligence (AI): AI refers to programs and machines that execute tasks with the characteristics of human intelligence Google Duplex Will Identify Itself as an AI Google Duplex Will Identify Itself as an AI Google Duplex was quite the talking point at I/O 2018, with serious morality questions being asked about the AI. However, Google has now made it clear Duplex will identify itself as not human. Read More , including problem-solving, forward planning, and learning. Broadly speaking, machines that can carry out human tasks in a manner we consider “intelligent.”
Machine Learning (ML): ML refers to a broad spectrum of the current applications of AI technologies focusing on the idea that machines with data access and the correct programming can learn for themselves. Broadly speaking, machine learning is a means to an end for achieving AI What Is Machine Learning? Google's Free Course Breaks It Down for You What Is Machine Learning? Google's Free Course Breaks It Down for You Google has designed a free online course to teach you the fundamentals of machine learning. Read More .

Machine learning and AI are deeply intertwined, and you can see how the terms see occasional misuse. The difference in meaning with regards to antivirus is an important distinction. Most (if not all) of the latest antivirus suites implement some form of machine learning, but some algorithms are more advanced than others.

Machine learning in antivirus technologies isn’t new. It is getting more intelligent, and is easier to use as a marketing tool now that the wider public is more aware of ML and AI.

How Security Companies Use AI in Antivirus

There are a few antivirus solutions that use advanced algorithms to protect your system, but the use of true AI is still rare. Still, there are several antivirus tools with excellent AI and ML implementations that show how the security industry is evolving to protect you from the latest threats.

1. Cylance Smart Antivirus

Cylance is a well-known name in machine learning and artificial intelligence cybersecurity. The enterprise-grade CylancePROTECT uses AI-techniques to protect a huge number of businesses, and they count several Fortune 100 organizations among their clientele. Cylance Smart Antivirus is their first foray into consumer antivirus products, bringing that enterprise-level AI protection into your home.

Cylance Smart Antivirus relies entirely on AI and ML to distinguish malware from legitimate data. The result is an antivirus that doesn’t bog your system down by constantly scanning and analyzing files. ( Or informing you of its status every 15-minutes Top Free Antivirus Apps Without Nag Screens and Bloatware Top Free Antivirus Apps Without Nag Screens and Bloatware Nagging antivirus apps are a huge pain. You don't have to put up with them, even for free. Here are the best antivirus programs that don't come with popups or bundled junk. Read More .) Rather, Cylance Smart Antivirus waits until the moment of execution and immediately kills the threat―without human intervention.

“Consumers deserve security software that is fast, easy to use, and effective,” said Christopher Bray, senior vice president, Cylance Consumer. “The consumer antivirus market is long overdue for a ground-breaking solution built on robust technology that allows them to control their security environment.”

Thanks for the shout out @sawaba I can vouch that the primary reason we launched Cylance Smart Antivirus is because our customers have told us they’ve grown frustrated with everything on the market now.

― Hiep Dang (@Hiep_Dang) June 19, 2018

Smart Antivirus does, however, have some downsides. Unlike other antivirus suites with active monitoring, Cylance Smart Antivirus allows you to visit potentially malicious sites. I assume this is confidence that the product will stop malicious downloads, but it doesn’t protect against phishing attacks or similar threats.

A single Cylance Smart Antivirus license costs $29 per year , while a $69 household pack lets you install on five different systems.

2. Deep Instinct D-Client

Deep Instinct uses deep learning (a machine learning technique) to detect “any file before it is accessed or executed” on your system. The Deep Instinct D-Client makes use of static file analysis in conjunction with a threat prediction model that allows it to eliminate malware and other system threats autonomously.

Deep Instinct’s D-Client uses vast quantities of raw data to continue improving its detection algorithms. Deep Instinct is one of the only companies with private deep learning infrastructure dedicated to improving their detection accuracy, too.

3. Avast Free Antivirus

For most people, Avast is a familiar name in security. Avast Free Antivirus is the most popular antivirus on the market, and its history of protections goes back decades. Avast Free Antivirus has been “using AI and machine learning for years” to protect users from evolving threats. In 2012, the Avast Research Lab announced three powerful backend tools for their products.

The “Malware Similarity Search” allows almost instantaneous categorization of huge samples of incoming malware. Avast Free Antivirus quickly analyzes similarities between existing malware files using both static and dynamic analysis.
“Evo-Gen” is similar “but a bit subtler in nature.” Evo-Gen is a genetic algorithm that works to find short and generic descriptions of malware in massive datasets.
“MDE” is a database that works on top of the indexed data, allowing heavy parallel access.

These three machine learning technologies collectively evolved as the foundation for Avast’s CyberCapture .

CyberCapture is a core feature of the Avast security suite, specifically targeting unknown malware and zero-days. When an unknown suspicious file enters a system, CyberCapture activates and immediately isolates the host system. The suspect file automatically uploads to an Avast cloud server for data analysis. Afterwards, the user receives a positive or negative notification regarding the status of the file. All the while, your data is feeding back into the algorithms to define further and enhance yours and others’ system security.

The Windows Defender Security Center for enterprise and business solutions will receive a phenomenal boost as Microsoft turns to artificial intelligence to bulk out its security. The 2017 WannaCry ransomworm ripped through Windows systems Prevent WannaCry Malware Variants by Disabling This Windows 10 Setting Prevent WannaCry Malware Variants by Disabling This Windows 10 Setting WannaCry has thankfully stopped spreading, but you should still disable the old, insecure protocol it exploited. Here's how to do it on your own computer in just a moment. Read More after hackers released a CIA trove of zero-day vulnerabilities into the wild.

Microsoft is creating a 400 million computer-strong machine learning network to build its next generation of security tools. The new AI-backed security features will start with its enterprise customers, but eventually filter down to Windows 10 systems for regular consumers. Windows Defender is constantly improving in other ways, too, and is now one of the top enterprise and consumer security solutions . The below image illustrates a snapshot of how Windows Defender machine learning protections works.

Is your antivirus suite more advanced than you realized? Machine learning and artificial intelligence are undoubtedly making larger inroads with security products. But their current prominence is more buzzword than effective deployment.

Try not to worry too much about whether your antivirus has AI or is implementing machine learning techniques. In the meantime, here’s a comparison of the best free antivirus products The 10 Best Free Anti-Virus Programs The 10 Best Free Anti-Virus Programs You must know by now: you need antivirus protection. Macs, Windows and Linux PCs all need it. You really have no excuse. So grab one of these ten and start protecting your computer! Read More for you to check out. AI or not, it is important to protect your system at all times.

With ever-increasing computational demand for deep learning, it is critical
to investigate the implications of the numeric representation and precision of
DNN model weights and activations on computational efficiency. In this work, we
explore unconventional narrow-precision floating-point representations as it
relates to inference accuracy and efficiency to steer the improved design of
future DNN platforms. We show that inference using these custom numeric
representations on production-grade DNNs, including GoogLeNet and VGG, achieves
an average speedup of 7.6x with less than 1% degradation in inference accuracy
relative to a state-of-the-art baseline platform representing the most
sophisticated hardware using single-precision floating point. To facilitate the
use of such customized precision, we also present a novel technique that
drastically reduces the time required to derive the optimal precision
configuration.

Deep learning approaches have been rapidly adopted across a wide range of
fields because of their accuracy and flexibility, but require large labeled
training datasets. This presents a fundamental problem for applications with
limited, expensive, or private data (i.e. small data), such as human pose and
behavior estimation/tracking which could be highly personalized. In this paper,
we present a semi-supervised data augmentation approach that can synthesize
large scale labeled training datasets using 3D graphical engines based on a
physically-valid low dimensional pose descriptor. To evaluate the performance
of our synthesized datasets in training deep learning-based models, we
generated a large synthetic human pose dataset, called ScanAva using 3D scans
of only 7 individuals based on our proposed augmentation approach. A
state-of-the-art human pose estimation deep learning model then was trained
from scratch using our ScanAva dataset and could achieve the pose estimation
accuracy of 91.2% at PCK0.5 criteria after applying an efficient domain
adaptation on the synthetic images, in which its pose estimation accuracy was
comparable to the same model trained on large scale pose data from real humans
such as MPII dataset and much higher than the model trained on other synthetic
human dataset such as SURREAL.

Recently, deep learning(DL) methods have been proposed for the low-dose
computed tomography(LdCT) enhancement, and obtain good trade-off between
computational efficiency and image quality. Most of them need large number of
pre-collected ground-truth/high-dose sinograms with less noise, and train the
network in a supervised end-to-end manner. This may bring major limitations on
these methods because the number of such low-dose/high-dose training sinogram
pairs would affect the network's capability and sometimes the ground-truth
sinograms are hard to be obtained in large scale. Since large number of
low-dose ones are relatively easy to obtain, it should be critical to make
these sources play roles in network training in an unsupervised learning
manner. To address this issue, we propose an unsupervised DL method for LdCT
enhancement that incorporates unlabeled LdCT sinograms directly into the
network training. The proposed method effectively considers the structure
characteristics and noise distribution in the measured LdCT sinogram, and then
learns the proper gradient of the LdCT sinogram in a pure unsupervised manner.
Similar to the labeled ground-truth, the gradient information in an unlabeled
LdCT sinogram can be used for sufficient network training. The experiments on
the patient data show effectiveness of the proposed method.

The employment of high-performance servers and GPU accelerators for training
deep neural network models have greatly accelerated recent advances in machine
learning (ML). ML frameworks, such as TensorFlow, MXNet, and Caffe2, have
emerged to assist ML researchers to train their models in a distributed
fashion. However, correctly and efficiently utilizing multiple machines and
GPUs is still not a straightforward task for framework users due to the
non-trivial correctness and performance challenges that arise in the
distribution process. This paper introduces Parallax, a tool for automatic
parallelization of deep learning training in distributed environments. Parallax
not only handles the subtle correctness issues, but also leverages various
optimizations to minimize the communication overhead caused by scaling out.
Experiments show that Parallax built atop TensorFlow achieves scalable training
throughput on multiple CNN and RNN models, while requiring little effort from
its users.

Machine reading comprehension is a task to model relationship between passage
and query. In terms of deep learning framework, most of state-of-the-art models
simply concatenate word and character level representations, which has been
shown suboptimal for the concerned task. In this paper, we empirically explore
different integration strategies of word and character embeddings and propose a
character-augmented reader which attends character-level representation to
augment word embedding with a short list to improve word representations,
especially for rare words. Experimental results show that the proposed approach
helps the baseline model significantly outperform state-of-the-art baselines on
various public benchmarks.

Methods: Singleshot EPI is an efficient encoding technique, but does not lend
itself well to high-resolution imaging due to severe distortion artifacts and
blurring. While msEPI can mitigate these artifacts, high-quality msEPI has been
elusive because of phase mismatch arising from shot-to-shot physiological
variations which disrupt the combination of the multiple-shot data into a
single image. We employ Deep Learning to obtain an interim magnitude-valued
image with minimal artifacts, which permits estimation of image phase
variations due to shot-to-shot physiological changes. These variations are then
included in a Joint Virtual Coil Sensitivity Encoding (JVC-SENSE)
reconstruction to utilize data from all shots and improve upon the ML solution.

Results: Our combined ML + physics approach enabled R=8-fold acceleration
from 2 EPI-shots while providing 1.8-fold error reduction compared to the
MUSSELS, a state-of-the-art reconstruction technique, which is also used as an
input to our ML network. Using 3 shots allowed us to push the acceleration to
R=10-fold, where we obtained a 1.7-fold error reduction over MUSSELS.

Conclusion: Combination of ML and JVC-SENSE enabled navigator-free msEPI at
higher accelerations than previously possible while using fewer shots, with
reduced vulnerability to poor generalizability and poor acceptance of
end-to-end ML approaches.

The back-propagation algorithm is the cornerstone of deep learning. Despite
its importance, few variations of the algorithm have been attempted. This work
presents an approach to discover new variations of the back-propagation
equation. We use a domain specific lan- guage to describe update equations as a
list of primitive functions. An evolution-based method is used to discover new
propagation rules that maximize the generalization per- formance after a few
epochs of training. We find several update equations that can train faster with
short training times than standard back-propagation, and perform similar as
standard back-propagation at convergence.

We introduce a novel unsupervised loss function for learning semantic
segmentation with deep convolutional neural nets (ConvNet) when densely labeled
training images are not available. More specifically, the proposed loss
function penalizes the L1-norm of the gradient of the label probability vector
image , i.e. total variation, produced by the ConvNet. This can be seen as a
regularization term that promotes piecewise smoothness of the label probability
vector image produced by the ConvNet during learning. The unsupervised loss
function is combined with a supervised loss in a semi-supervised setting to
learn ConvNets that can achieve high semantic segmentation accuracy even when
only a tiny percentage of the pixels in the training images are labeled. We
demonstrate significant improvements over the purely supervised setting in the
Weizmann horse, Stanford background and Sift Flow datasets. Furthermore, we
show that using the proposed piecewise smoothness constraint in the learning
phase significantly outperforms post-processing results from a purely
supervised approach with Markov Random Fields (MRF). Finally, we note that the
framework we introduce is general and can be used to learn to label other types
of structures such as curvilinear structures by modifying the unsupervised loss
function accordingly.

Neuromorphic hardware tends to pose limits on the connectivity of deep
networks that one can run on them. But also generic hardware and software
implementations of deep learning run more efficiently for sparse networks.
Several methods exist for pruning connections of a neural network after it was
trained without connectivity constraints. We present an algorithm, DEEP R, that
enables us to train directly a sparsely connected neural network. DEEP R
automatically rewires the network during supervised training so that
connections are there where they are most needed for the task, while its total
number is all the time strictly bounded. We demonstrate that DEEP R can be used
to train very sparse feedforward and recurrent neural networks on standard
benchmark tasks with just a minor loss in performance. DEEP R is based on a
rigorous theoretical foundation that views rewiring as stochastic sampling of
network configurations from a posterior.

This paper demonstrates how to apply machine learning algorithms to
distinguish good stocks from the bad stocks. To this end, we construct 244
technical and fundamental features to characterize each stock, and label stocks
according to their ranking with respect to the return-to-volatility ratio.
Algorithms ranging from traditional statistical learning methods to recently
popular deep learning method, e.g. Logistic Regression (LR), Random Forest
(RF), Deep Neural Network (DNN), and the Stacking, are trained to solve the
classification task. Genetic Algorithm (GA) is also used to implement feature
selection. The effectiveness of the stock selection strategy is validated in
Chinese stock market in both statistical and practical aspects, showing that:
1) Stacking outperforms other models reaching an AUC score of 0.972; 2) Genetic
Algorithm picks a subset of 114 features and the prediction performances of all
models remain almost unchanged after the selection procedure, which suggests
some features are indeed redundant; 3) LR and DNN are radical models; RF is
risk-neutral model; Stacking is somewhere between DNN and RF. 4) The portfolios
constructed by our models outperform market average in back tests.

In a world of global trading, maritime safety, security and efficiency are
crucial issues. We propose a multi-task deep learning framework for vessel
monitoring using Automatic Identification System (AIS) data streams. We combine
recurrent neural networks with latent variable modeling and an embedding of AIS
messages to a new representation space to jointly address key issues to be
dealt with when considering AIS data streams: massive amount of streaming data,
noisy data and irregular timesampling. We demonstrate the relevance of the
proposed deep learning framework on real AIS datasets for a three-task setting,
namely trajectory reconstruction, anomaly detection and vessel type
identification.

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The Containers Micro-conference at Linux Plumbers is the yearly gathering of container runtime developers, kernel developers and container users. It is the one opportunity to have everyone in the same room to both look back at the past year in the container space and discuss the year ahead.

In the past, topics such as use of cgroups by containers, system call filtering and interception (Seccomp), improvements/additions of kernel namespaces, interaction with the Linux Security Modules (AppArmor, SELinux, SMACK), TPM based validation (IMA), mount propagation and mount API changes, uevent isolation, unprivileged filesystem mounts and more have been discussed in this micro-conference.

The LF Deep Learning Foundation, an umbrella organization of The Linux Foundation that supports and sustains open source innovation in artificial intelligence, machine learning, and deep learning, today announced five new members: Ciena, DiDi, Intel, Orange and Red Hat. The support of these new members will provide additional resources to the community to develop and expand open source AI, ML and DL projects, such as the Acumos AI Project, the foundation's comprehensive platform for AI model discovery, development and sharing.

One might think that memory allocation during system startup should not be difficult: almost all of memory is free, there is no concurrency, and there are no background tasks that will compete for memory. Even so, boot-time memory management is a tricky task. Physical memory is not necessarily contiguous, its extents change from system to system, and the detection of those extents may be not trivial. With NUMA things are even more complex because, in order to satisfy allocation locality, the exact memory topology must be determined. To cope with this, sophisticated mechanisms for memory management are required even during the earliest stages of the boot process.

One could ask: "so why not use the same allocator that Linux uses normally from the very beginning?" The problem is that the primary Linux page allocator is a complex beast and it, too, needs to allocate memory to initialize itself. Moreover, the page-allocator data structures should be allocated in a NUMA-aware way. So another solution is required to get to the point where the memory-management subsystem can become fully operational.

In the early days, Linux didn't have an early memory allocator; in the 1.0 kernel, memory initialization was not as robust and versatile as it is today. Every subsystem initialization call, or simply any function called from start_kernel(), had access to the starting address of the single block of free memory via the global memory_start variable. If a function needed to allocate memory it just increased memory_start by the desired amount. By the time v2.0 was released, Linux was already ported to five more architectures, but boot-time memory management remained as simple as in v1.0, with the only difference being that the extents of the physical memory were detected by the architecture-specific code. It should be noted, though, that hardware in those days was much simpler and memory configurations could be detected more easily.

The kernel's out-of-memory (OOM) killer is summoned when the system runs short of free memory and is unable to proceed without killing one or more processes. As might be expected, the policy decisions around which processes should be targeted have engendered controversy for as long as the OOM killer has existed. The 4.19 development cycle is likely to include a new OOM-killer implementation that targets control groups rather than individual processes, but it turns out that there is significant disagreement over how the OOM killer and control groups should interact.

To simplify a bit: when the OOM killer is invoked, it tries to pick the process whose demise will free the most memory while causing the least misery for users of the system. The heuristics used to make this selection have varied considerably over time — it was once remarked that each developer who changes the heuristics makes them work for their use case while ruining things for everybody else. In current kernels, the heuristics implemented in oom_badness() are relatively simple: sum up the amount of memory used by a process, then scale it by the process's oom_score_adj value. That value, found in the process's /proc directory, can be tweaked by system administrators to make specific processes more or less attractive as an OOM-killer target.

No OOM-killer implementation is perfect, and this one is no exception. One problem is that it does not pay attention to how much memory a particular user has allocated; it only looks at specific processes. If user A has a single large process while user B has 100 smaller ones, the OOM killer will invariably target A's process, even if B is using far more memory overall. That behavior is tolerable on a single-user system, but it is less than optimal on a large system running containers on behalf of multiple users.

The LF Deep Learning Foundation, whose mission is to support and sustain open source innovation in artificial intelligence, machine learning, and deep learning, announced five new members: Ciena, DiDi, Intel, Orange and Red Hat.

“We are very pleased to build off the launch momentum of the LF Deep Learning Foundation and welcome new members with vast resources and technical expertise to support our growing community and ecosystem of AI projects,” said Lisbeth McNabb, Chief Operating Officer of The Linux Foundation.

Mazin Gilbert, Vice President of Advanced Technology and Systems at AT&T, has also been elected to the role of Governing Board Chair of LF Deep Learning. This position leads the board in supporting various AI and ML open source projects, including infrastructure and support initiatives related to each project.

“The Deep Learning Foundation is a significant achievement by the open source community to drive harmonization among tools and platforms in deep learning and artificial intelligence,” said Mazin Gilbert, Vice President of Advanced Technology and Systems at AT&T. “This effort will enable an open marketplace of analytics and machine learning capabilities to help expedite adoption and deployments of DL solutions worldwide.”

On this episode of the podcast we continue a conversation we started with Haben Girma, an advocate for equal rights for people with disabilities, regarding the value of tech accessibility. Melanie and Mark talk with her about common challenges and best practices when considering accessibility in technology design and development. Bottom line - we need one solution that works for all.

Haben Girma

The first Deafblind person to graduate from Harvard Law School, Haben Girma advocates for equal opportunities for people with disabilities. President Obama named her a White House Champion of Change, and Forbes recognized her in Forbes 30 Under 30. Haben travels the world consulting and public speaking, teaching clients the benefits of fully accessible products and services. Haben is a talented storyteller who helps people frame difference as an asset. She resisted society’s low expectations, choosing to create her own pioneering story. Because of her disability rights advocacy she has been honored by President Obama, President Clinton, and many others. Haben is also writing a memoir that will be published by Grand Central Publishing in 2019. Learn more at habengirma.com.

Today visionary thinker, futurist and filmmaker Mitch Schultz joins Dr. Kelly to explore humanity as we approach the technological singularity. What is the singularity, and what does it mean for humanity?
Explore a transdisciplinary approach at the intersection of the arts, cognitive psychology, deep learning, and philosophy. Guided by Consciousness, Evolution, and Story. Beginning with what conscious state of being (terrestrial and universal) is perceiving. Followed the one consta ...

Today visionary thinker, futurist and filmmaker Mitch Schultz joins Dr. Kelly to explore humanity as we approach the technological singularity. What is the singularity, and what does it mean for humanity?
Explore a transdisciplinary approach at the intersection of the arts, cognitive psychology, deep learning, and philosophy. Guided by Consciousness, Evolution, and Story. Beginning with what conscious state of being (terrestrial and universal) is perceiving. Followed the one consta ...

DeepMotion, a pioneer in the field of Motion Intelligence, announced today that DeepMotion Neuron, the first tool for completely procedural, physical character animation, has launched for presale. The breakthrough cloud application trains digital characters to develop physical intelligence using advanced AI, physics and deep learning. With guidance and practice, digital characters can now achieve adaptive […]

From classrooms to campus infrastructure, higher education is rapidly adapting to cloud technology. So it’s no surprise that academic faculty and staff were well represented among panelists and attendees at this year’sGoogle Cloud Next. Several of our more than 500 breakout sessions at Next spoke to the needs of higher education, as as did critical announcements like our partnership with the National Institutes of Health to make make public biomedical datasets available to researchers. Here are ten major themes that came out our higher education sessions at Next:

Collaborating across campuses. Learning technologists from St. Norbert College, Lehigh University, University of Notre Dame, and Indiana University explained how G Suite and CourseKit, Google’s new integrated learning management tool, are helping teachers and students exchange ideas.

Navigating change.Academic IT managers told stories of how they’ve overcome the organizational challenges of cloud migration and offered some tips for others: start small, engage key stakeholders, and take advantage of Google’s teams of engineers and representatives, who are enthusiastic and knowledgeable allies. According to Joshua Humphrey, Team Lead, Enterprise Computing, Georgia State University, "We've been using GCP for almost three years now and we've seen an average yearly savings of 44%. Whenever people ask why we moved to the cloud this is what we point to. Usability and savings."

Fostering student creativity. In our higher education booth at Next, students demonstrated projects that extended their learning beyond the classroom. For example, students at California State University at San Bernardino built a mobile rover that checks internet connectivity on campus, and students at High Tech High used G Suite and Chromebooks to help them create their own handmade soap company.

Reproducing scientific research. Science is built on consistent, reliable, repeatable findings. Academic research panelists at the University of Michigan are using Docker on Compute Engine to containerize pipeline tools so any researcher can run the same pipeline without having to worry about affecting the final outcome.

Powering bioinformatics. Today’s biomedical research often requires storing and processing hundreds of terabytes of data. Teams at SUNY Downstate, Northeastern, and the University of South Carolina demonstrated how they used BigQuery and Compute Engine to build complex simulations and manage huge datasets for neuroscience, epidemiology, and environmental research.

Accelerating genomics research. Moving data to the cloud enables faster processing to test more hypotheses and uncover insights. Researchers from Stanford, Duke, and Michigan showed how they streamlined their genomics workloads and cut months off their processing time using GCP.

Democratizing access to deep learning. AutoML Vision, Natural Language, and Translation, all in beta, were announced at Next and can help researchers build custom ML models without specialized knowledge in machine learning or coding. As Google’s Chief Scientist of AI and Machine Learning Fei-Fei Li noted in her blog post, Google’s aim “is to make AI not just more powerful, but more accessible.”

Transforming LMS analytics. Scalable tools can turn the data collected by learning management systems and student information services into insights about student behavior. Google’s strategic partnership with Unizen allows a consortium of universities to integrate data and learning sciences, while Ivy Tech used ML Engine to build a predictive algorithm to improve student performance in courses.

Personalizing machine learning and AI for student services. We’re seeing a growing trend of universities investigating AI to create virtual assistants. Recently Strayer University shared with us how they used Dialogflow to do just that, and at Next, Carnegie Mellon walked us through their process of building SARA, a socially-aware robot assistant.

Strengthening security for academic IT: Natural disasters threaten on-premise data centers, with earthquakes, flooding, and hurricanes demanding robust disaster-recovery planning. Georgia State, the University of Minnesota, and Stanford’s Graduate School of Business shared how they improved the reliability and cost-efficiency of their data backup by migrating to GCP.

We've been using GCP for almost three years now and we've seen an average yearly savings of 44%. Whenever people ask why we moved to the cloud this is what we point to: usability and savingsJoshua Humphrey Enterprise Computing, Georgia State University

Recently, Apple’s stock price rose to the point where the company’s market valuation was above $1 trillion, the first U.S. company to reach that benchmark. Subsequently, numerous articles were published describing Apple’s journey to this point and why it got there. Most people describe Apple as a technology company. They make technology products: iPhones, iPads, Macs, etc. These are all computing devices. But there is another way to think of Apple and what kind of company they are as well as how they became so successful.

Neil Cybart, an analyst over at Above Avalon, likes to describe Apple as a design company focused on building useful tools for people. Of the latest round of profiles on Apple reaching a $1 trillion market valuation, he writes:

Despite supposedly being about chronicling how Apple went from near financial collapse in the late 1990s to a trillion-dollar market cap, a number of articles did not include any mention of Jony Ive [Apple’s Chief Design Officer], or even design for that matter. To not include Jony Ive in an article about Apple’s last 20 years is unfathomable, demonstrating a clear misunderstanding of Apple’s culture and the actual reasons that contributed to Apple’s success. Simply put, such profiles failed in their pursuit of describing Apple’s journey to a trillion dollars. Apple is where it is today because of design – placing an emphasis on how Apple products are used. Every other item or variable is secondary. [emphasis added]

As long as I have followed computers people have complained that Apple’s hardware is substandard. Other companies like Dell, Gateway, Acer, and Lenovo, had long been making computers that were “better” than Apple’s hardware. Apple’s value has always been selling good hardware coupled with premium software. But for a long time that was not appreciated by the market and Apple almost went bankrupt as a result.

The “Speeds and Feeds” Era for Data Analysis

When I was growing up, computers were all about so-called “speeds and feeds”. The only things people talked about were the megahertz of their processor or how many megabytes of RAM a computer had. A computer with a higher megahertz CPU was by definition better than a computer with a lower megahertz CPU. More RAM was better than less RAM and more disk space was better than less disk space. It was easy to compare different computers because we had quantitative metrics to go by. The hardware itself was a commodity and discussion about software was nonexistent because every computer ran the same software: Windows.

We are very much in the “speeds and feeds” era for data analysis right now. There is tremendous focus on and fascination with the tools and machinery underlying data analysis. Deep learning is only one such example, along with an array of related machine learning tools. Web sites like Kaggle promote a culture of “performance” where the person who can cobble together the most accurate algorithm is a winner. It’s easy to compare different algorithms to each other because there is often a single metric of performance that we can easily agree to compare.

Serious investment is being made in improving algorithms to make them more accurate, efficient, and powerful. We need these algorithms to be better so that we can have self-driving cars, intelligent assistants, fraud detection, and music discovery. Even the hardware itself is being optimized to improve the performance of these specific algorithms. This is the call of “more gigahertz, more RAM, more disk space” of our time. As easy hardware wins are fading into the past (as shown by Intel’s struggle), the focus is on improving the performance of machine learning software running on top of it.

All of this is necessary if we want to reap the benefits of machine learning algorithms in our daily lives. But if the computing industry has anything to teach the data science industry, it’s that perhaps the more interesting stuff is yet to come. Furthermore, it suggests that the companies (and perhaps individuals) with the best speeds and feeds will not necessarily be the winners.

What Comes Next?

Today, it could be argued that the most profitable “computer” in the world is the iPhone, which to be sure, has better “speeds and feeds” than any computer from my childhood. But it is by no means the fastest computer today. Nor does it have the most RAM, the most disk space, or the best graphics. How can that be?

Of course, the focus of computing changed from desktop to laptop to mobile, in part due to the great advancement in chip technology and miniaturization. So the benefit was not in greater speeds and feeds, but rather in smaller sizes for the same speeds and feeds. With these smaller, more personal, devices, the software and the design of the system became of greater importance. People were not using these devices to “crunch numbers” or do complex, but highly specialized, tasks. Rather, they were using them to do everyday tasks, like checking email, surfing the web, and communicating with friends. These were not business machines; they were for the mass market.

Arguable, the emphasis that Apple places on design has made it the most successful computer company of today because design is what creates the best user experience today in the mass market. Data science remains a niche area of work today even though its popularity and application has exploded over just a few years. It’s difficult for me to see how it might move into a mass market position, but I can see more and more people doing and consuming data analysis in the future. As the population of data analysis consumers grows, I think people will become less focused on accuracy and prediction metrics and more focused on whether a given analysis achieves a specified goal. In other words, data analyses will have to be designed to accomplish a certain task. The better individuals are at designing good data analyses, the more successful they will be.

Andrew Ng is a great fan of reading research papers as a long term investment in your own study (On Life, Creativity, And Failure about Andrew Ng). Anyone who has worked in our field (AI, Machine Learning) can attest to that. AI is a complex and a rapidly evolving field. It’s a challenge to stay up to date with the latest technical details.Based on my experience, in this post, I discuss how you can stay up to date by learning from the community. From a personal perspective, I work in two niche areas – Enterprise AI and my teaching for AI and IoT at the University of Oxford.My strategy for personal investment in my study is: to study a broad set of topics in the following four categories:Tutorials and GithubLeaders and networksDeep Learning papersInterview questionsI have tried to create a concise list below which should give you depth for AI and Deep Learning. This list also reflects my personal study bias (for example Python) – hence is not comprehensive.I am thankful to all the people/sources listed here for their willingness to share insights which have helped my own learning over the years.Read the full list of resources (by Ajit Joakar), here.See More

From classrooms to campus infrastructure, higher education is rapidly adapting to cloud technology. So it’s no surprise that academic faculty and staff were well represented among panelists and attendees at this year’sGoogle Cloud Next. Several of our more than 500 breakout sessions at Next spoke to the needs of higher education, as as did critical announcements like our partnership with the National Institutes of Health to make make public biomedical datasets available to researchers. Here are ten major themes that came out our higher education sessions at Next:

Collaborating across campuses. Learning technologists from St. Norbert College, Lehigh University, University of Notre Dame, and Indiana University explained how G Suite and CourseKit, Google’s new integrated learning management tool, are helping teachers and students exchange ideas.

Navigating change.Academic IT managers told stories of how they’ve overcome the organizational challenges of cloud migration and offered some tips for others: start small, engage key stakeholders, and take advantage of Google’s teams of engineers and representatives, who are enthusiastic and knowledgeable allies. According to Joshua Humphrey, Team Lead, Enterprise Computing, Georgia State University, "We've been using GCP for almost three years now and we've seen an average yearly savings of 44%. Whenever people ask why we moved to the cloud this is what we point to. Usability and savings."

Fostering student creativity. In our higher education booth at Next, students demonstrated projects that extended their learning beyond the classroom. For example, students at California State University at San Bernardino built a mobile rover that checks internet connectivity on campus, and students at High Tech High used G Suite and Chromebooks to help them create their own handmade soap company.

Reproducing scientific research. Science is built on consistent, reliable, repeatable findings. Academic research panelists at the University of Michigan are using Docker on Compute Engine to containerize pipeline tools so any researcher can run the same pipeline without having to worry about affecting the final outcome.

Powering bioinformatics. Today’s biomedical research often requires storing and processing hundreds of terabytes of data. Teams at SUNY Downstate, Northeastern, and the University of South Carolina demonstrated how they used BigQuery and Compute Engine to build complex simulations and manage huge datasets for neuroscience, epidemiology, and environmental research.

Accelerating genomics research. Moving data to the cloud enables faster processing to test more hypotheses and uncover insights. Researchers from Stanford, Duke, and Michigan showed how they streamlined their genomics workloads and cut months off their processing time using GCP.

Democratizing access to deep learning. AutoML Vision, Natural Language, and Translation, all in beta, were announced at Next and can help researchers build custom ML models without specialized knowledge in machine learning or coding. As Google’s Chief Scientist of AI and Machine Learning Fei-Fei Li noted in her blog post, Google’s aim “is to make AI not just more powerful, but more accessible.”

Transforming LMS analytics. Scalable tools can turn the data collected by learning management systems and student information services into insights about student behavior. Google’s strategic partnership with Unizen allows a consortium of universities to integrate data and learning sciences, while Ivy Tech used ML Engine to build a predictive algorithm to improve student performance in courses.

Personalizing machine learning and AI for student services. We’re seeing a growing trend of universities investigating AI to create virtual assistants. Recently Strayer University shared with us how they used Dialogflow to do just that, and at Next, Carnegie Mellon walked us through their process of building SARA, a socially-aware robot assistant.

Strengthening security for academic IT: Natural disasters threaten on-premise data centers, with earthquakes, flooding, and hurricanes demanding robust disaster-recovery planning. Georgia State, the University of Minnesota, and Stanford’s Graduate School of Business shared how they improved the reliability and cost-efficiency of their data backup by migrating to GCP.

We've been using GCP for almost three years now and we've seen an average yearly savings of 44%. Whenever people ask why we moved to the cloud this is what we point to: usability and savingsJoshua Humphrey Enterprise Computing, Georgia State University

Based on their collective comments, I think there is a good chance for reconciliation and a working consensus between “reformers” and those of us who have had major problems with reform policies, implementation, and assumptions. There seems to be a common emphasis on the following approaches to improving student and school performance:

The centrality of curriculum and instruction

High-quality materials

Building the processes schools and districts (or CMO’s) use for school improvement, such as improving the capacity at each school for continuous growth

Attracting higher caliber teachers, improved induction, career ladders, and leadership, and a continued attention to improving performance for all

Alternate pathways for high school graduation, including career and technical education

Increased funding

Striking a balance between school and local control and district and state expectations and support

Avoiding the harsher anti-public-school and anti-teacher rhetoric

Looking to both traditional public schools and charter schools for models of high performance

These ideas also drove our efforts in California—where I live and once headed up the state education agency—to improve performance.

Mike’s, Peter’s, and Sandy’s willingness to be honest about problems with the reform movement and their sincere attempt to find common ground is to be commended. Both charters and traditional public schools need to improve, and there is a growing agreement on what that takes.

Mike writes that preparing students for democracy should be one of the purposes driving any improvement effort. There is a growing interest in civics and civic engagement in the country, and excellent exemplars now exist among charters (Democracy Prep, for example) and traditional public schools.

My only caveat is to add one more important purpose of education: the classic goal of a liberal education to help enrich each student’s life so they can reach their individual potential and develop character and a high moral stance. Mike mentions in passing literature, history, and the humanities as helping to find out how the world works, and he makes a glancing reference to character development in the service of citizenship. Yet I think this goal of broadening individual perspectives to lead a more fulfilling life should be explicitly expressed. (For a discussion of this point, see my essay titled “The Big Picture: The Three Goals of Public Education.”)

Mike also deserves kudos for promoting rigorous career and technical education as a pathway for students not bound for a four-year college. For a school, district, or state, the preparation-for-work goal should be to maximize the number of students prepared for a four-year college (or a pathway on which they transfer to one from a two-year school), and to prepare all others for a specific vocation. Presently, the country is preparing about 40 percent for four-year colleges. Even if we increase that to 50 percent (a formidable goal), that still leaves a large number of students not served. Most current policy at state and district levels basically ignores these students and assumes almost all can and should be prepared for a four-year college.

I do agree with those who are wary of an early placement test because of the danger of a premature choice, as we should give some students the chance to change perspectives in later grades. As one alternative, schools in the San Diego Unified School District have a Linked Learning college program that’s combined with a career path in which students who follow the latter early on are able to shift to the four-year-college track at a later time.

Many of Mike’s comments on literacy are also spot-on, including the importance of early foundation skills, and then content and vocabulary, as the major drivers of improving comprehension, as opposed to an over-emphasis on “comprehension skills.” One of the major deficiencies of annual statewide literacy tests is the lack of connection to content and the resulting default to comprehension strategies. Louisiana, for example, is attempting to correct this situation.

From our perspective, too many reformers are still too wedded to a strict accountability model based on a faulty theory of change. The initial reform paradigm was a simple structural leverage approach: Define student performance standards (mainly for accountability purposes, not to inform instructional improvement), assess whether the standards were being met, publicize those outcomes, provide consequences for results (bad and good), get out of the way of individual schools, and let pressure from harsh consequences and competition, especially from charters and parents, force improvement.

This strategy proved to be flawed in several respects and thus didn’t produce the hoped-for results.

First, highly simplistic is the assumption that individual schools, if given freedom from district control and spurred by competition and consequences, would figure out how to improve on their own, and it proved false for most schools. Many reformers now realize that the missing ingredient in that paradigm was direct attention to and support for the nuts and bolts of school improvement: curriculum, instructional materials, professional development, team building, principal and teacher leadership, effective district (or CMO) assistance, and help with getting these elements to cohere, as well as proper funding for those efforts. (Peter Cunningham’s response to Mike’s essay therefore deserves praise for asserting the importance of funding if improvement is to occur.) By comparison, the indirect method of attempting to improve performance by standards, primarily test-based assessments, and consequential accountability turned out to be a much weaker way to influence school performance, and it produced considerable collateral damage.

Another erroneous assumption underlying this simple reform paradigm was that educators would not improve unless compelled or pressured by fear of consequences or competition. Actually, most educators want to improve, but many did not know how, did not receive proper support, or were subject to leaders who were motivated by a test-and-punish philosophy relying on fear instead of the more engaging build-and-support approach. Appealing to teachers as professionals and engaging them in the work of improvement produces results; pressuring them often backfires. Deming and Drucker still apply.

Yet many reformers want to retain or strengthen accountability with consequences and embed the more direct approach in high-stakes accountability. The two strategies conflict because they stem from two radically different theories of how to encourage professionals to improve. More often than not, pressure and competition detract from high performance. High-stakes testing encourages schools or districts to become too fixated on test results and test items, to the detriment of deep learning and learning progressions. Campbell’s law is relevant; consequential accountability encourages educators to game the system, outright cheat, or become detached from the commitment to deeper learning and long-term continuous improvement by concentrating on short-term test results. Some reformers retort that teaching to the test and test prep are fine if complex skills are tested. But the tests don’t meet that standard. Dan Koretz’s The Testing Charade and Jim Popham’s work exemplify the problems with focusing on standardized test results, which are not of a fine enough grain size to help instruction.

As an example, tests don’t reflect the emerging idea of the importance of learning progressions, such as the development of proportional thinking in mathematics. These should be driving curriculum, instruction, classroom student assessment, and personalization. (See the recently released and excellent “Illustrative Mathematics” for a free curriculum based on learning progressions that was developed by Bill McCallum—one of the authors of Common Core Math—and his team for math in grades six through eight, and which received a top rating from EdReports.) Many reformers have advocated for more personalized, adaptive instruction. One impediment was the U.S. Department of Education’s original refusal to allow the Smarter Balanced Assessment Consortium to develop an adaptive test on broader strands across grades so students could adjust to higher or lower positions on these broader learning progressions. They insisted that the tests be limited to the standards of a particular grade.

Annual test results are a useful warning light and offer useful information about subgroups, but a whole array of formative evaluations, the use of instructional tasks as assessments, and teacher and student judgments are necessary to focus on what is needed to improve student performance. All too often, annual assessments drive instruction in superficial and shallow ways, instead of being one tool in the service of deeper learning. Many charters and traditional public schools, which live and die by annual test results, have become test-prep machines, narrowing the curriculum and harming student’s future performance. Also problematic is the tendency for some charter schools to trumpet bogus results by such ploys as not backfilling open slots over time and creating a rarified cohort. Competition and fear of consequences have similarly infected many traditional public schools with the same disease, including outright cheating or fiddling with who takes the test.

Finally, radical decentralization did not produce the results as advertised. The theory was based in part on the idea that districts were a main part of the problem of low performance. Districts were consumed by politics, stakeholder resistance, and/or bureaucratic inefficiencies, and were thought to be ineffective because they were top-down compliance oriented, or incapable of or not interested in improving results, but rather in protecting turf. They couldn’t or wouldn’t change. Decentralizing to individual schools, preferably charters, however, did not solve the problem of district effectiveness or individual schools and teachers needing support. Districts (or the central support structure in CMO’s) turn out to be crucial players in improving schools. Instead of end-running them, efforts should be made to improve their performance, and should be modeled after what our best districts have done. Contrary to the argument that districts were incapable of change, there is a growing number of districts in this country that have significantly improved their ability to support school improvement

Districts in California—such as Long Beach (which only has a handful of charters), Garden Grove, Elk Grove, and Sanger—as well as comparable districts across the rest of the country, were able to engender school-site improvement by reorienting their management philosophy. They made the difficult shift from compliance orientation to support and engagement, but still insisted on high expectations—which, if not met, initiated discussions on how to improve. They placed solid curricula and effective classroom instruction at the center of improvement efforts and built supportive structures and processes to facilitate instructional improvement with impressive results. That strategy should guide improvement policies. Instead of giving up on districts, we should agree on and support approaches and polices geared to help the laggards improve.

Bravo also to Mike’s suggestion that teacher quality and teaching are not the only determinants of high student performance. Curricula, good materials, support processes, money, and community efforts are all also crucial. While reformers are now stressing the importance of curriculum and instruction, they and many traditional school leaders have not thought deeply enough about the complex school processes necessary to improve classroom instruction. Mike alludes to “professional development,” but an effective improvement strategy is much more complex than that. Educators and policymakers need to concentrate on how to develop coherence among coaching, professional development, team building, use of instructional materials, a broad array of classroom formative assessment techniques, teacher and principal leadership, support for struggling students, and what districts must do to support those efforts.

It is also gratifying to see many pro-public-school reformers become sensitive to and willing to oppose privatization forces high-jacking their rhetoric to replace or drastically cut funding for public schools, or to squelch teacher unions, as has happened in many Republican-led states and at the national level. Most reformers now resist the canard that the choice is between reformers’ policies favoring students or the status quo favoring adult and union interests. Both pro-public-education reformers and the anti-reform camp want to improve the quality of our schools; the debate is over which policies or strategies will best accomplish that goal.

Many of us also agree with reformers’ proposals to concentrate more on the front end of the teacher pipeline. Welcome are suggestions to increase the quality of new teachers by strengthening teacher preparation programs, in part via higher admissions standards, and by lengthening the initial time for granting tenure, with streamlined due process protections as part of career-ladder progressions.

For existing teachers, many reformers have criticized the almost exclusive reform emphasis on firing the worst teachers by test-based and intricate principal evaluations. The effort was ruined by the use of faulty assessments and processes, and the policy itself detracted from more positive efforts to raise the performance of all staff. Moreover, concentrating on the worst often neglected supporting the best through such approaches as embedding the most effective teachers in a learning community and expanding their influence

Rewarding excellent teachers with more cash has not worked and has caused collateral damage by lowering morale and jeopardizing team building. There is a simple way out of this: Pay the best teachers more, but also have them take on additional supportive roles. Career ladders and teacher-leadership positions need to become much more prevalent, as some reformers have argued. Convincing a top teacher to stay in the profession improves student and school performance much more than firing a laggard.

That’s not to say that the worst teachers should not be fired or counseled out. There are some excellent examples of effective teacher evaluation strategies, such as those in California’s San Jose and San Juan public school districts, where teachers have helped design and implement the programs. When there is teacher buy-in and evaluation is embedded in a comprehensive school improvement effort that includes the participation of teacher leaders at the school, the rates of dismissal or resignations of the weaker teachers is actually higher. Incompetent teachers can’t hide in group efforts; those who can improve do so, and the many who can’t just resign. Conversely, having principals spend an inordinate amount of time and paperwork conducting multiple classroom visits of every teacher for the purpose of formal evaluation severely hampers their more productive role of organizing their schools. Even the best teachers are willing to accept improvement advice as part of a collaborative improvement effort; but they tend to shut down, narrow their teaching, or resist when it is part of a formal evaluation process, especially from someone whom they don’t believe is more skilled than they are.

There are many more issues which could be discussed, but I hope that this commentary helps illuminate areas of agreement, areas needing further discussion, and areas that are still in dispute.

Bill Honig has been a practicing educator for more than forty-five years. He has taught in the inner-city schools of San Francisco, served as a local superintendent in Marin County, and was appointed to the State Board of Education by California governor Jerry Brown during his first term. In 1983, Honig was elected California state superintendent of public instruction, a position he held for ten years. In 1995, he founded the Consortium on Reaching Excellence. And he recently served as Vice-Chair of the California Instructional Quality Commission.

The views expressed herein represent the opinions of the author and not necessarily the Thomas B. Fordham Institute.

This week at the Data-Centric Innovation Summit, Intel laid out their near-term Xeon roadmap and plans to augment their AVX-512 instruction set to boost machine learning performance. "This dramatic performance improvement and efficiency - up to twice as fast as the current generation - is delivered by using a single instruction to handle INT8 convolutions for deep learning inference workloads which required three separate AVX-512 instructions in previous generation processors."